國立虎尾科技大學 |

Exploring the Big Data and Machine Learning Framing Concepts for a Predictive Classification Model = = Explorando Los Conceptos De Encuadre De Big Data Y Machine Learning Para Un Modelo De Clasificacion Predictivo.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Exploring the Big Data and Machine Learning Framing Concepts for a Predictive Classification Model =/
其他題名:	Explorando Los Conceptos De Encuadre De Big Data Y Machine Learning Para Un Modelo De Clasificacion Predictivo.
其他題名:	Explorando Los Conceptos De Encuadre De Big Data Y Machine Learning Para Un Modelo De Clasificacion Predictivo
作者:	Hidalgo, Jasson Josue.
面頁冊數:	1 online resource (126 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355926620

Exploring the Big Data and Machine Learning Framing Concepts for a Predictive Classification Model = = Explorando Los Conceptos De Encuadre De Big Data Y Machine Learning Para Un Modelo De Clasificacion Predictivo.
Hidalgo, Jasson Josue.

Exploring the Big Data and Machine Learning Framing Concepts for a Predictive Classification Model =Explorando Los Conceptos De Encuadre De Big Data Y Machine Learning Para Un Modelo De Clasificacion Predictivo.Explorando Los Conceptos De Encuadre De Big Data Y Machine Learning Para Un Modelo De Clasificacion Predictivo - 1 online resource (126 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (D.C.S.)--Colorado Technical University, 2018.

Includes bibliographical references

Understanding big data and machine learning framing concepts to develop a predictive classification model was essential for the growth and evolution of data science and other industries. Many data scientists conducted extensive research in the area of big data and machine learning to develop predictive classification models. In 2017, data scientists created predictive models by using models that solely featured text or only with images. However, this study presents for the first time, the big data and machine learning framing concepts needed to develop a predictive classification model to combine images and text to improve the predictive classification for 2D, gray-scale images such as dental application and text such as the patient medical history. In this study, framing concepts are a list of requirements, processes, tools, and common best practices. The study identified 16 major themes related to the big data and machine learning framing concepts, 17 more themes to support the architecture, and 8 themes related to the future of big data and machine learning to improve the predictive classification for 2D, gray-scale images and text. The big data and machine learning framing concepts presented in this study will allow future researchers to develop predictive classification models to assist doctors to use images and patient data for diagnosis, to assist criminal investigators in utilizing images and investigations notes or reports, airplane or vehicle accidents investigations, general manufacturing, retail industry, big data analytics, and many other fields. The research methodology used in this study was qualitative research. Nine experienced professionals participated in the study.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355926620Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Exploring the Big Data and Machine Learning Framing Concepts for a Predictive Classification Model = = Explorando Los Conceptos De Encuadre De Big Data Y Machine Learning Para Un Modelo De Clasificacion Predictivo.
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520 $a Understanding big data and machine learning framing concepts to develop a predictive classification model was essential for the growth and evolution of data science and other industries. Many data scientists conducted extensive research in the area of big data and machine learning to develop predictive classification models. In 2017, data scientists created predictive models by using models that solely featured text or only with images. However, this study presents for the first time, the big data and machine learning framing concepts needed to develop a predictive classification model to combine images and text to improve the predictive classification for 2D, gray-scale images such as dental application and text such as the patient medical history. In this study, framing concepts are a list of requirements, processes, tools, and common best practices. The study identified 16 major themes related to the big data and machine learning framing concepts, 17 more themes to support the architecture, and 8 themes related to the future of big data and machine learning to improve the predictive classification for 2D, gray-scale images and text. The big data and machine learning framing concepts presented in this study will allow future researchers to develop predictive classification models to assist doctors to use images and patient data for diagnosis, to assist criminal investigators in utilizing images and investigations notes or reports, airplane or vehicle accidents investigations, general manufacturing, retail industry, big data analytics, and many other fields. The research methodology used in this study was qualitative research. Nine experienced professionals participated in the study.
520 $a Para el crecimiento y la evolucion de la ciencia de los datos y otras industrias, era esencial entender los conceptos de encuadre de Big Data y Machine Learning y desarrollar un modelo de clasificacion predictiva. Muchos cientificos de datos realizaron una amplia investigacion en el area de big data y machine learning para desarrollar modelos de clasificacion predictiva. En 2017, los cientificos de datos crearon modelos predictivos utilizando modelos que solo functional utilizando texto o solo imagenes. Sin embargo, este estudio presenta por primera vez los conceptos de encuadre de big data y machine learning necesarios para desarrollar un modelo predictivo de clasificacion que combine imagenes y texto y mejorar la clasificacion predictiva de imagenes 2D en escala de grises, como las aplicaciones dentales y texto como el historial medico del paciente. En este estudio, los conceptos de encuadre son una lista de requisitos, procesos, herramientas y mejores practicas comunes. El estudio identifico 16 temas principales relacionados con los conceptos de encuadre de big data y machine learning, 17 temas mas para apoyar la arquitectura del sistem y 8 temas relacionados con el futuro de big data y machine learning para mejorar la clasificacion predictiva para imagenes 2D en escala de grises y texto. Los conceptos de big data y machine learning presentados en este estudio permitiran a los futuros investigadores desarrollar modelos predictivos de clasificacion para ayudar a los medicos a utilizar imagenes y datos de pacientes para el diagnostico, ayudar a los investigadores a utilizar imagenes e informes de investigaciones, investigaciones de accidentes de aeroplanos o vehiculos, fabricacion general, industria minorista, analisis de big data y muchos otros campos. La metodologia de investigacion utilizada en este estudio fue la investigacion cualitativa. Nueve profesionales con experiencia participaron en el estudio.
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